Browsing by Author "Moate, Chris"

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    Modelling real‐world effects in near‐field SAR collections for compressive sensing
    (Institution of Engineering and Technology (IET), 2025-01) Price, George A. J.; Andre, Daniel; Moate, Chris; Yuen, Peter; Finnis, Mark
    The ability to control sidelobes in a SAR image is critical to forming images that are useful for interpretation and exploitation. QinetiQ has developed the RIBI sensing system, which utilises a distributed coherent array of sensors to produce multistatic images. These systems require techniques from outside the traditional radar domain to utilise the theoretical resolution possible in synthesising a coherent aperture from multiple disparate collections. This paper develops previously published work on using compressive sensing techniques to suppress sidelobes in SAR images to develop a higher‐fidelity measurement model. Using Cranfield University's GBSAR System a series, experimental measurements are conducted, and image estimation techniques are applied to this real data. It demonstrates an improvement in recovery performance over an isotropic measurement matrix, and discusses areas which require further development.
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    Sidelobe suppression techniques for near-field multistatic SAR
    (MDPI, 2023-01-09) Price, George A. J.; Moate, Chris; Andre, Daniel; Yuen, Peter W. T.
    Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible. Since 2015, QinetiQ have developed the RIBI system, which uses multiple UAS to perform short-range multistatic collections, and this requires novel near-field processing to mitigate the high sidelobes observed and form actionable imagery. This paper applies a number of algorithms to assess image reconstruction of simulated near-field multistatic SAR with an aim to suppress sidelobes observed in the RIBI system, investigating techniques including traditional SAR processing, regularised linear regression, compressive sensing. In these simulations presented, Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding all show the ability to suppress sidelobes while preserving accuracy of scatterer RCS. This has also lead to a novel processing approach for reconstructing SAR images based on the observed Elastic net and Iterative Hard Thresholding performance, mitigating weaknesses to generate an improved combined approach. The relative strengths and weaknesses of the algorithms are discussed, as well as their application to more complex real-world imagery.